A class-modular feedforward neural network for handwriting recognition

Since the conventional feedforward neural networks for character recognition have been designed to classify a large number of classes with one large network structure, inevitably it poses the very complex problem of determining the optimal decision boundaries for all the classes involved in a high-dimensional feature space. Limitations also exist in several aspects of the training and recognition processes. This paper introduces the class modularity concept to the feedforward neural network classifier to overcome such limitations. In the class-modular concept, the original K-classification problem is decomposed into K 2-classification subproblems. A modular architecture is adopted which consists of K subnetworks, each responsible for discriminating a class from the other K−1 classes. The primary purpose of this paper is to prove the effectiveness of class-modular neural networks in terms of their convergence and recognition power. Several cases have been studied, including the recognition of handwritten numerals (10 classes), English capital letters (26 classes), touching numeral pairs (100 classes), and Korean characters in postal addresses (352 classes). The test results confirmed the superiority of the class-modular neural network and the interesting aspects on further investigations of the class modularity paradigm.